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An effective algorithm for genealogical graph partitioning

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Abstract

This study proposes a novel Approximately Balanced Tree Partitioning Algorithm (TPA) to overcome the significant challenges in genealogical data management, encompassing the storage, maintenance, and interpretation of complex familial networks. Our TPA is adept at modularizing and simplifying intricate relationships in genealogical graphs into logically succinct tree structures, reducing user cognitive load and enhancing the utility of genealogical data in real applications like hereditary disease research, forensic investigation, and consanguinity counseling. In addition, TPA prioritizes structural closeness in partitioning to avoid misleading insights from unrelated data points and maintain a balance of node distribution to prevent workload and communication overheads in distributed graph data processing systems. The effectiveness of our algorithm is demonstrated through extensive experiments on four real-world genealogical datasets, substantiating its superiority over five state-of-the-art rival models in dealing with the complex and rapidly expanding landscape of genealogical data.

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Availability of data and materials

The datasets and code used during this study are available upon reasonable request to the authors.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under grant 62120106008, and in part by the Fundamental Research Funds for the Central Universities under grant JZ2023HGTB0270.

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Authors and Affiliations

  1. Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Hefei, 230009, China

    Shaojing Sheng, Zan Zhang & Xindong Wu

  2. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China

    Shaojing Sheng & Zan Zhang

  3. School of Computer Science and Technology, Anhui University, Hefei, 230601, China

    Peng Zhou

  4. Research Center for Knowledge Engineering, Zhejiang Lab, Hangzhou, 311121, China

    Xindong Wu

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  1. Shaojing Sheng
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  2. Zan Zhang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, validation, and analysis were performed by Shaojing Sheng, Zan Zhang, Peng Zhou and Xindong Wu. The first draft of the manuscript was written by Shaojing Sheng, and all authors commented on previous versions of the manuscript. The second draft is revised by Shaojing Sheng based on the reviewers’ comments, and all authors commented on the rectifications. All authors read and approved the final manuscript.

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Correspondence to Xindong Wu.

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Sheng, S., Zhang, Z., Zhou, P. et al. An effective algorithm for genealogical graph partitioning. Appl Intell 54, 1798–1817 (2024). https://doi.org/10.1007/s10489-023-05265-1

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